U.S. patent application number 11/832262 was filed with the patent office on 2007-11-22 for automatic segmentation in speech synthesis.
This patent application is currently assigned to AT&T Corp.. Invention is credited to Alistair D. Conkie, Yeon-Jun Kim.
Application Number | 20070271100 11/832262 |
Document ID | / |
Family ID | 28457009 |
Filed Date | 2007-11-22 |
United States Patent
Application |
20070271100 |
Kind Code |
A1 |
Conkie; Alistair D. ; et
al. |
November 22, 2007 |
AUTOMATIC SEGMENTATION IN SPEECH SYNTHESIS
Abstract
Systems and methods for automatically segmenting speech
inventories. A set of Hidden Markov Models (HMMs) are initialized
using bootstrap data. The HMMs are next re-estimated and aligned to
produce phone labels. The phone boundaries of the phone labels are
then corrected using spectral boundary correction. Optionally, this
process of using the spectral-boundary-corrected phone labels as
input instead of the bootstrap data is performed iteratively in
order to further reduce mismatches between manual labels and phone
labels assigned by the HMM approach.
Inventors: |
Conkie; Alistair D.;
(Morristown, NJ) ; Kim; Yeon-Jun; (Whippany,
NJ) |
Correspondence
Address: |
AT&T CORP.
ROOM 2A207
ONE AT&T WAY
BEDMINSTER
NJ
07921
US
|
Assignee: |
AT&T Corp.
New York
NY
|
Family ID: |
28457009 |
Appl. No.: |
11/832262 |
Filed: |
August 1, 2007 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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|
10341869 |
Jan 14, 2003 |
7266497 |
|
|
11832262 |
Aug 1, 2007 |
|
|
|
60369043 |
Mar 29, 2002 |
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Current U.S.
Class: |
704/256 ;
704/E13.001; 704/E13.009 |
Current CPC
Class: |
G10L 13/06 20130101 |
Class at
Publication: |
704/258 ;
704/E13.001 |
International
Class: |
G10L 15/14 20060101
G10L015/14 |
Claims
1. A system that concatenates speech units to produce synthetic
speech, the system comprising: a module configured to train a set
of Hidden Markov Models (HMMs) using seed data in a first
iteration; a module configured to align the set of HMMs to produce
segmented unit labels; and a module configured to adjust boundaries
of the unit labels using spectral boundary correction, wherein the
unit labels having adjusted boundaries are used to concatenate
speech units to synthesize speech.
2. The system in claim 1, wherein the module configured to train a
set of Hidden Markov Models further: initializes the set of HMMs
using at least one of hand-labeled bootstrapped data,
speaker-independent HMM bootstrapped data, and flat start data;
re-estimates the set of HMMs; and performs an embedded
re-estimation on the set of HMMs.
3. The system of claim 1, wherein the module configured to adjust
boundaries of the unit labels using spectral boundary correction
further adjusts boundaries of the unit labels within specified time
windows.
4. The system of claim 1, wherein the module configure to adjust
boundaries of the unit labels using spectral boundary correction
further: combines HMM-based segmentation with spectral features to
reduce misalignments between target unit boundaries and boundaries
assigned by the HMM-based segmentation.
5. The system of claim 1, wherein the module configured to adjust
boundaries of the unit labels using spectral boundary correction
further: identifies context-dependent time windows around the unit
boundaries, wherein the unit boundaries include one or more of: a
vowel-to-vowel boundary; a vowel-to-nasal boundary; a
vowel-to-voiced stop boundary; a vowel-to-liquid boundary; a
vowel-to-unvoiced stop boundary; a vowel-to-voiced fricative
boundary; an unvoiced stop-to-vowel boundary; a nasal-to-vowel
boundary; a voiced stop-to-vowel boundary a liquid-to-vowel
boundary; an unvoiced fricative-to-vowel boundary; and a voiced
fricative-to-vowel boundary.
6. The system of claim 5, wherein context-dependent time windows
are empirically determined by adjacent phones.
7. The system of claim 1, further comprising a module configured to
use the unit labels whose boundaries have been adjusted by spectral
boundary correction as input for a next iteration of: training a
set of HMMs; aligning the set of HMMs using a Viterbi alignment to
produce phone labels; and adjusting boundaries of the unit labels
using spectral boundary correction.
8. A system having a speech inventory that includes phone labels
that are concatenated to form synthetic speech, the system
comprising: a module configured to perform a first alignment on a
trained set of HMMs to produce phone labels that are segmented,
wherein each phone label has a spectral boundary; a module
configured to perform spectral boundary correction on the phone
labels, wherein spectral boundary correction re-aligns each
spectral boundary using bending points of spectral transitions; and
a module configured to synthesize speech using the phone labels
having spectral boundary correction.
9. The system of claim 8, wherein the module configured to perform
a first alignment on a trained set of HMMs to produce phone labels
that are segmented further bootstraps the set of HMMs with at least
one of speaker-dependent HMMs and speaker-independent HMMs.
10. The system of claim 8, wherein the module configured to perform
a first alignment on a trained set of HMMs to produce phone labels
that are segmented further: initializes the set of HMMs;
re-estimates the set of HMMs; and performs embedded re-estimation
on the set of HMMs.
11. The system of claim 8, wherein the module configured to perform
a first alignment on a trained set of HMMs to produce phone labels
that are segmented further performs a Viterbi alignment on the
trained set of HMMs to produce phone labels that are segmented.
12. The system of claim 10, wherein the module configured to
perform a first alignment on a trained set of HMMs to produce phone
labels that are segmented and the module configured to perform
spectral boundary correction on the phone labels perform
iteratively.
13. The system of claim 12, further comprising a module configured
to train the set of HMMs using phone labels having boundaries that
have been re-aligned using spectral boundary correction.
14. The system of claim 8, wherein the module configured to perform
spectral boundary correction on the phone labels further performs
spectral boundary correction on the phone labels within a
context-dependent time window.
15. The system of claim 14, further comprising a module configured
to determine empirically the context-dependent time window using
adjacent phones.
16. The system of claim 14, wherein each spectral boundary is
between a first phone class and a second phone class.
17. A computing device that segments phone labels to reduce
misalignments in order to improve synthetic speech when the phone
labels are concatenated, the computing device comprising: a module
configured to train a set of HMMs using one of a specific speaker's
hand-labeled speech data and speaker-independent speech data; a
module configured to segment the trained set of HMMs using a first
alignment to produce phone labels, wherein each phone label has a
spectral boundary; a module configured to use a weighted slope
metric to identify bending points of spectral transitions, wherein
each bending point corresponds to a spectral boundary, a module
configured to correct a particular spectral boundary of a
particular phone label if the particular spectral boundary does not
coincide with a particular bending point; and a module configured
to synthesize speech using the phone labels with corrected spectral
boundaries.
18. The computing device of claim 17, wherein the module configured
to use a weighted slope metric to identify bending points of
spectral transitions further applies the weighted slope metric
within context-dependent time windows such that spurious spectral
boundaries are not applied to the phone labels.
19. The computing device of claim 18, further comprising a module
configured to retrain the set of HMMs using the phone labels that
have been corrected using the weighted slope metric.
20. The computing device of claim 18, wherein each spectral
boundary is defined by a first phone class and a second phone
class, wherein the first phone class and the second phone class
include at least one of a vowel, an unvoiced stop, a voiced stop,
an unvoiced fricative, a voiced fricative, a liquid class and a
nasal class.
21. The computing device of claim 18, further comprising a module
configured to determine context-dependent time windows empirically.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 10/341,869, filed Jan. 14, 2003, which claims
the benefit of U.S. Provisional Patent Application Ser. No.
60/369,043 entitled "System and Method of Automatic Segmentation
for Text to Speech Systems" and filed Mar. 29, 2002, which are
incorporated herein by reference in their entirety.
BACKGROUND OF THE INVENTION
[0002] 1. The Field of the Invention
[0003] The present invention relates to systems and methods for
automatic segmentation in speech synthesis. More particularly, the
present invention relates to systems and methods for automatic
segmentation in speech synthesis by combining a Hidden Markov Model
(HMM) approach with spectral boundary correction.
[0004] 2. The Relevant Technology
[0005] One of the goals of text-to-speech (TTS) systems is to
produce high-quality speech using a large-scale speech corpus. TTS
systems have many applications and, because of their ability to
produce speech from text, can be easily updated to produce a
different output by simply altering the textual input. Automated
response systems, for example, often utilize TTS systems that can
be updated in this manner and easily configured to produce the
desired speech. TTS systems also play an integral role in many
automatic speech recognition (ASR) systems.
[0006] The quality of a TTS system is often dependent on the speech
inventory and on the accuracy with which the speech inventory is
segmented and labeled. The speech or acoustic inventory usually
stores speech units (phones, diphones, half-phones, etc.) and
during speech synthesis, units are selected and concatenated to
create the synthetic speech. In order to achieve high quality
synthetic speech, the speech inventory should be accurately
segmented and labeled in order to avoid noticeable errors in the
synthetic speech.
[0007] Obtaining a well segmented and labeled speech inventory,
however, is a difficult and time consuming task. Manually
segmenting or labeling the units of a speech inventory cannot be
performed in real time speeds and may require on the order of 200
times real time to properly segment a speech inventory.
Accordingly, it will take approximately 400 hours to manually label
2 hours of speech. In addition, consistent segmentation and
labeling of a speech inventory may be difficult to achieve if more
than one person is working on a particular speech inventory. The
ability to automate the process of segmenting and labeling speech
would clearly be advantageous.
[0008] In the development of both ASR and TTS systems, automatic
segmentation of a speech inventory plays an important role in
significantly reducing reduce the human effort that would otherwise
be require to build, train, and/or segment speech inventories.
Automatic segmentation is particularly useful as the amount of
speech to be processed becomes larger.
[0009] Many TTS systems utilize a Hidden Markov Model (HMM)
approach to perform automatic segmentation in speech synthesis. One
advantage of a HMM approach is that it provides a consistent and
accurate phone labeling scheme. Consistency and accuracy are
critical for building a speech inventory that produces intelligible
and natural sounding speech. Consistent and accurate segmentation
is particularly useful in a TTS system based on the principles of
unit selection and concatenative speech synthesis.
[0010] Even though HMM approaches to automatic segmentation in
speech syntheses have been successful, there is still room for
improvement regarding the degree of automation and accuracy. As
previously stated, there is a need to reduce the time and cost of
building an inventory of speech units. This is particularly true as
a demand for more synthetic voices, including customized voices,
increases. This demand has been primarily satisfied by performing
the necessary segmentation work manually, which significantly
lengthens the time required to build the speech inventories.
[0011] For example, hand-labeled bootstrapping may require a month
of labeling by a phonetic expert to prepare training data for
speaker-dependent HMMs (SD HMMs). Although hand-labeled
bootstrapping provides quite accurate phone segmentation results,
the time required to hand label the speech inventory is
substantial. In contrast, bootstrapping automatic segmentation
procedures with speaker-independent HMMs (SI HMMs) instead of SD
HMMs reduces the manual workload considerably while keeping the
HMMs stable. Even when SI HMMs are used, there is still room for
improving the segmentation accuracy and degree of segmentation
automation.
[0012] Another concern with regard to automatic segmentation is
that the accuracy of the automatic segmentation determines, to a
large degree, the quality of speech that is synthesized by unit
selection and concatenation. An HMM-based approach is somewhat
limited in its ability to remove discontinuities at concatenation
points because the Viterbi alignment used in an HMM-based approach
tries to find the best HMM sequence when given a phone
transcription and a sequence of HMM parameters rather than the
optimal boundaries between adjacent units or phones. As a result,
an HMM-based automatic segmentation system may locate a phone
boundary at a different position than expected, which results in
mismatches at unit concatenation points and in speech
discontinuities. There is therefore a need to improve automatic
segmentation.
BRIEF SUMMARY OF THE INVENTION
[0013] The present invention overcomes these and other limitations
and relates to systems and methods for automatically segmenting a
speech inventory. More particularly, the present invention relates
to systems and methods for automatically segmenting phones and more
particularly to automatically segmenting a speech inventory by
combining an HMM-based approach with spectral boundary
correction.
[0014] In one embodiment, automatic segmentation begins by
bootstrapping a set of HMMs with speaker-independent HMMs. The set
of HMMs is initialized, re-estimated, and aligned to produce the
labeled units or phones. The boundaries of the phone or unit labels
that result from the automatic segmentation are corrected using
spectral boundary correction. The resulting phones are then used as
seed data for HMM initialization and re-estimation. This process is
performed iteratively.
[0015] A phone boundary is defined, in one embodiment, as the
position where the maximal concatenation cost concerning spectral
distortion is located. Although Euclidean distance between mel
frequency cepstral coefficients (MFCCs) is often used to calculate
spectral distortions, the present invention utilizes a weighted
slop metric. The bending point of a spectral transition often
coincides with a phone boundary. The spectral-boundary-corrected
phones are then used to initialize, re-estimate and align the HMMs
iteratively. In other words, the labels that have been re-aligned
using spectral boundary correction are used as feedback for
iteratively training the HMMs. In this manner, misalignments
between target phone boundaries and boundaries assigned by
automatic segmentation can be reduced.
[0016] Additional features and advantages of the invention will be
set forth in the description which follows, and in part will be
obvious from the description, or may be learned by the practice of
the invention. The features and advantages of the invention may be
realized and obtained by means of the instruments and combinations
particularly pointed out in the appended claims. These and other
features of the present invention will become more fully apparent
from the following description and appended claims, or may be
learned by the practice of the invention as set forth
hereinafter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0017] A more particular description of the invention briefly
described above will be rendered by reference to specific
embodiments thereof which are illustrated in the appended drawings.
Understanding that these drawings depict only typical embodiments
of the invention and are not therefore to be considered limiting of
its scope, the invention will be described and explained with
additional specificity and detail through the use of the
accompanying drawings in which:
[0018] FIG. 1 illustrates a text-to-speech system that converts
textual input to audible speech;
[0019] FIG. 2 illustrates an exemplary method for automatic
segmentation using spectral boundary correction with an HMM
approach; and
[0020] FIG. 3 illustrates a bending point of a spectral transition
that coincides with a phone boundary in one embodiment.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0021] Speech inventories are used, for example, in text-to-speech
(TTS) systems and in automatic speech recognition (ASR) systems.
The quality of the speech that is rendered by concatenating the
units of the speech inventory represents how well the units or
phones are segmented. The present invention relates to systems and
methods for automatically segmenting speech inventories and more
particularly to automatically segmenting a speech inventory by
combining an HMM-based segmentation approach with spectral boundary
correction. By combining an HMM-based segmentation approach with
spectral boundary correction, the segmental quality of synthetic
speech in unit-concatenative speech synthesis is improved.
[0022] An exemplary HMM-based approach to automatic segmentation
usually includes two phases: training the HMMs, and unit
segmentation using the Viterbi alignment. Typically, each phone or
unit is defined as an HMM prior to unit segmentation and then
trained with a given phonetic transcription and its corresponding
feature vector sequence. TTS systems often require more accuracy in
segmentation and labeling than do ASR systems.
[0023] FIG. 1 illustrates an exemplary TTS system that converts
text to speech. In FIG. 1, the TTS system 100 converts the text 110
to audible speech 118 by first performing a linguistic analysis 112
on the text 110. The linguistic analysis 112 includes, for example,
applying weighted finite state transducers to the text 110. In
prosodic modeling 114, each segment is associated with various
characteristics such as segment duration, syllable stress, accent
status, and the like. Speech synthesis 116 generates the synthetic
speech 118 by concatenating segments of natural speech from a
speech inventory 120. The speech inventory 120, in one embodiment,
usually includes a speech waveform and phone labeled data.
[0024] The boundary of a unit (phone, diphone, etc.) for
segmentation purposes is defined as being where one unit ends and
another unit begins. For the speech to be coherent and natural
sounding, the segmentation must occur as close to the actual unit
boundary as possible. This boundary often naturally occurs within a
certain time window depending on the class of the two adjacent
units. In one embodiment of the present invention, only the
boundaries within these time windows are examined during spectral
boundary correction in order to obtain more accurate unit
boundaries. This prevents a spurious boundary from being
inadvertently recognized as the phone boundary, which would lead to
discontinuities in the synthetic speech.
[0025] FIG. 2 illustrates an exemplary method for automatically
segmenting phones or units and illustrates three examples of seed
data to begin the initialization of a set of HMMs. Seed data can be
obtained using, for example: hand-labeled bootstrap 202,
speaker-independent (SI) HMM bootstrap 204, and a flat start 206.
Hand-labeled bootstrapping, which utilizes a specific speaker's
hand-labeled speech data, results in the most accurate HMM modeling
and is often called speaker-dependent HMM (SD HMM). While SD HMMs
are generally used for automatic segmentation in speech synthesis,
they have the disadvantage of being quite time-consuming to
prepare. One advantage of the present invention is to reduce the
amount of time required to segment the speech inventory.
[0026] If hand-labeled speech data is available for a particular
language, but not for the intended speaker, bootstrapping with SI
HMM alignment is the best alternative. In one embodiment, SI HMMs
for American English, trained with the TIMIT speech corpus, were
used in the preparation of seed phone labels. With the resulting
labels, SD HMMs for an American male speaker were trained to
provide the segmentation for building an inventory of synthesis
units. One advantage of bootstrapping with SI HMMs is that all of
the available speech data can be used as training data if
necessary.
[0027] In this example, the automatic segmentation system includes
ARPA phone HMMs that use three-state left-to-right models with
multiple mixture of Gaussian density. In this example, standard HMM
input parameters, which include twelve MFCCs (Mel frequency
cepstral coefficients), normalized energy, and their first and
second order delta coefficients, are utilized.
[0028] Using one hundred randomly chosen sentences, the SD HMMs
bootstrapped with SI HMMs result in phones being labeled with an
accuracy of 87.3% (<20 ms, compared to hand labeling). Many
errors are caused by differences between the speaker's actual
pronunciations and the given pronunciation lexicon, i.e., errors by
the speaker or the lexicon or effects of spoken language such as
contractions. Therefore, speaker-individual pronunciation
variations have to be added to the lexicon.
[0029] FIG. 2 illustrates a flow diagram for automatic segmentation
that combines an HMM-based approach with iterative training and
spectral boundary correction. Initialization 208 occurs using the
data from the hand-labeled bootstrap 202, the SI HMM bootstrap 204,
or from a flat start 206. After the HMMs are initialized, the HMMs
are re-estimated (210). Next, embedded re-estimation 212 is
performed. These actions--initialization 208, re-estimation 210,
and embedded re-estimation 212--are an example of how HMMs are
trained from the seed data.
[0030] After the HMMs are trained, a Viterbi alignment 214 is
applied to the HMMs in one embodiment to produce the phone labels
216. After the HMMs are aligned, the phones are labeled and can be
used for speech synthesis. In FIG. 2, however, spectral boundary
correction is applied to the resulting phone labels 216. Next, the
resulting phones are trained and aligned iteratively. In other
words, the phone labels that have been re-aligned using spectral
boundary correction are used as input to initialization 208
iteratively. The hand-labeled bootstrapping 202, SI HMM
bootstrapping 204, and the flat start 206 are usually used the
first time the HMMs are trained. Successive iterations use the
phone labels that have been aligned using spectral boundary
correction 218.
[0031] The motivation for iterative HMM training is that more
accurate initial estimates of the HMM parameters produce more
accurate segmentation results. The phone labels that result from
bootstrapping with SI HMMs are more accurate than the original
input (seed phone labels). For this reason, for tuning the SD HMMs
to produce the best results, the phone labels resulting from the
previous iteration and corrected using spectral boundary correction
218 are used as the input for HMM initialization 208 and
re-estimation 210, as shown in FIG. 2. This procedure is iterated
to fine-tune the SD HMMs in this example.
[0032] After several rounds of iterative training that includes
spectral boundary correction, mismatches between manual labels and
phone labels assigned by an HMM-based approach will be considerably
reduced. For example, when the HMM training procedure illustrated
in FIG. 2 was iterated five times in one example, an accuracy of
93.1% was achieved, yielding a noticeable improvement in synthesis
quality. The accuracy of phone labeling in a few speech samples
alone cannot predict synthetic quality itself. The stop condition
for iterative training, therefore, is defined as the point when no
more perceptual improvement of synthesis quality can be
observed.
[0033] A reduction of mismatches between phone boundary labels is
expected when the temporal alignment of the feed-back labeling is
corrected. Phone boundary corrections can be done manually or by
rule-based approaches. Assuming that the phone labels assigned by
an HMM-based approach are relatively accurate, automatic phone
boundary correction concerning spectral features improves the
accuracy of the automatic segmentation.
[0034] One advantage of the present invention is to reduce or
minimize the audible signal discontinuities caused by spectral
mismatches between two successive concatenated units. In
unit-concatenative speech synthesis, a phone boundary can be
defined as the position where the maximal concatenation cost
concerning spectral distortion, i.e., the spectral boundary, is
located. The Euclidean distance between MFCCs is most widely used
to calculate spectral distortions. As MFCCs were likely used in the
HMM-based segmentation, the present embodiment uses instead the
weighted slope metric (see Equation (1) below). d .function. ( S L
, S R ) = u E .times. E S L - E S R + i = 1 K .times. .times. u
.function. ( i ) .function. [ .DELTA. S L .function. ( i ) -
.DELTA. S R .function. ( i ) ] 2 ( 1 ) ##EQU1##
[0035] In this example, S.sup.L and S.sup.R are 256 point FFTs
(fast Fourier transforms) divided into K critical bands. The
S.sup.L and S.sup.R vectors represent the spectrum to the left and
the right of the boundary, respectively. E.sub.S.sub.L and
E.sub.S.sub.R are spectral energy, .DELTA..sub.S.sub.L(i) and
.DELTA..sub.S.sub.R (i) are the ith critical band spectral slopes
of S.sup.L and S.sup.R (see FIG. 3), and u.sub.E, u(i) are
weighting factors for the spectral energy difference and the ith
spectral transition.
[0036] Spectral transitions play an important role in human speech
perception. The bending point of spectral transition, i.e., the
local maximum of
.SIGMA..sub.i=1.sup.Ku(i)[.DELTA..sub.S.sub.L(i)-.DELTA..sub.S.sub.R(i)].-
sup.2, often coincides with a phone boundary. FIG. 3, which
illustrates adjacent spectral slopes, more fully illustrates the
bending point of a spectral transition. In this example, the
spectral slope 304 corresponds to the ith critical band of S.sup.L,
and the spectral slope 306 corresponds to the ith critical band of
S.sup.R. The bending point 302 of the spectral transition usually
coincides with a phone boundary. Using spectral boundaries
identified in this fashion, spectral boundary correction 218 can be
applied to the phone labels 216, as illustrated in FIG. 2.
[0037] In the present embodiment, |E.sub.S.sub.L-E.sub.S.sub.R|,
which is the absolute energy difference in Equation (1), is
modified to distinguish K critical bands, as in Equation (2): E S L
- E S R = j = 1 K .times. .times. w .function. ( j ) * .times. E S
L .function. ( j ) - E S R .function. ( j ) ( 2 ) ##EQU2## where
w(j) is the weight of the jth critical band. This is because each
phone boundary is characterized by energy changes in different
bands of the spectrum.
[0038] Although there is a strong tendency for the largest peak to
occur at the correct phone boundary, the automatic detector
described above may produce a number of spurious peaks. To minimize
the mistakes in the automatic spectral boundary correction, a
context-dependent time window in which the optimal phone boundary
is more likely to be found is used. The phone boundary is checked
only within the specified context-dependent time window.
[0039] Temporal misalignment tends to vary in time depending on the
contexts of two adjacent phones. Therefore, the time window for
finding the local maximum of spectral boundary distortion is
empirically determined, in this embodiment, by the adjacent phones
as illustrated in the following table. This table represents
context-dependent time windows (in ms) for spectral boundary
correction (V: Vowel, P: Unvoiced stop, B: Voiced stop, S: Unvoiced
fricative, Z: Voiced fricative, L: Liquid, N: Nasal).
TABLE-US-00001 Time window BOUNDARY Time window (ms) BOUNDARY (ms)
V-V -4.5 .+-. 50 P-V -1.6 .+-. 30 V-N -4.8 .+-. 30 N-V 0 .+-. 30
V-B -13.9 .+-. 30 B-V 0 .+-. 20 V-L -23.2 .+-. 40 L-V 11.1 .+-. 30
V-P 2.2 .+-. 20 S-V 2.7 .+-. 20 V-Z -15.8 .+-. 30 Z-V 15.4 .+-.
40
[0040] The present invention relates to a method for automatically
segmenting phones or other units by combining HMM-based
segmentation with spectral features using spectral boundary
correction. Misalignments between target phone boundaries and
boundaries assigned by automatic segmentation are reduced and
result in more natural synthetic speech. In other words, the
concatenation points are less noticeable and the quality of the
synthetic speech is improved.
[0041] The embodiments of the present invention may comprise a
special purpose or general purpose computer including various
computer hardware, as discussed in greater detail below.
Embodiments within the scope of the present invention may also
include computer-readable media for carrying or having
computer-executable instructions or data structures stored thereon.
Such computer-readable media can be any available media that can be
accessed by a general purpose or special purpose computer. By way
of example, and not limitation, such computer-readable media can
comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,
magnetic disk storage or other magnetic storage devices, or any
other medium which can be used to carry or store desired program
code means in the form of computer-executable instructions or data
structures and which can be accessed by a general purpose or
special purpose computer. When information is transferred or
provided over a network or another communications connection
(either hardwired, wireless, or a combination of hardwired or
wireless) to a computer, the computer properly views the connection
as a computer-readable medium. Thus, any such connection is
properly termed a computer-readable medium. Combinations of the
above should also be included within the scope of computer-readable
media.
[0042] Computer-executable instructions include, for example,
instructions and data which cause a general purpose computer,
special purpose computer, or special purpose processing device to
perform a certain function or group of functions.
Computer-executable instructions also include program modules which
are executed by computers in stand alone or network environments.
Generally, program modules include routines, programs, objects,
components, data structures, etc. that perform particular tasks or
implement particular abstract data types. Computer-executable
instructions, associated data structures, and program modules
represent examples of the program code means for executing steps of
the methods disclosed herein. The particular sequence of such
executable instructions or associated data structures represents
examples of corresponding acts for implementing the functions
described in such steps.
[0043] The present invention may be embodied in other specific
forms without departing from its spirit or essential
characteristics. The described embodiments are to be considered in
all respects only as illustrative and not restrictive. The scope of
the invention is, therefore, indicated by the appended claims
rather than by the foregoing description. All changes which come
within the meaning and range of equivalency of the claims are to be
embraced within their scope.
* * * * *